Conventional fuzzy controllers have drawbacks when the controlled object is in a steady state. FIG. 3 is a block diagram which shows a conventional fuzzy controller F arranged in a feedback system. In this diagram, 1 is an error generator, 2 is a differentiator circuit, 3 is a fuzzy inference unit, and 5 is the controlled object e.g. a machine or other device. The error generator 1 calculates the error e between the present feedback value Y, which is received from a sensor on the controlled object 5, and the target value r, which is supplied by a reference data generator 4. The differentiator circuit 2 calculates the differential .DELTA.e of the error e. .DELTA.e is the amount of variation of error e over time. Error e and its differential, .DELTA.e, are then provided as inputs for the fuzzy inference unit 3. A fuzzy inference is then performed on the inputs to determine the value of the actuating signal S. The actuating signal value S is then supplied to the controlled object 5.
FIG. 4 shows the control response waveform which is obtained during feedback control as described above. Over time, the present feedback value Y approaches a desired control value r.
FIG. 5 shows a set of membership functions based on the error e which originates in temperature control. Seven membership functions exist, corresponding to a range of error e, from -100.degree. C. to +100.degree. C.
In control dynamics, there are two states of variation: a transient state in which the error e is fluctuating and a steady state in which the error e remains stable. The above arrangement, however, gives rise to certain drawbacks, particularly when the controlled object is in a steady state of operation.
In particular, in the transient state, the amplitudes of the variations in the sequential values of error e are large, so that any fluctuation in the grade of the antecedent is readily apparent. Thus, memberships for the error e are easily determined by the fuzzy inference unit 3. In the steady state, however, the sequential values of the error e are relatively small (e.g. 0.degree. C., +1.degree. C. and -2.degree. C.) and the amplitude of variation will thereby also be small. The fluctuations in the grade of the antecedent will virtually vanish, and the corresponding output values will hardly vary at all. In other words, only the steady-state error remains. Thus, normal fuzzy control becomes impossible to perform when the object remains in a steady state.
A potential solution to the problem is to employ variations of narrow amplitudes to define the membership functions. However, even though the amplitude of variation in the resulting value of error e would be small, noticeable fluctuations in the grade of the antecedent within those amplitudes would still occur. Such a solution would also expand the number of membership functions and the number of corresponding rules, so that appreciably more processing time would be required to set the values and perform the inferences.